Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
Forfattere
Geir Wæhler GustavsenSammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Forfattere
Eva Skarbøvik Jukka Aroviita Jens Fölster Anne Lyche Solheim Katarina Kyllmar Katri Rankinen Brian KronvangSammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Forfattere
Lars Sandved DalenSammendrag
No abstract has been registered
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Cornelya KlutschSammendrag
No abstract has been registered
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Cornelya KlutschSammendrag
No abstract has been registered
Forfattere
Florencia A. Yannelli Chloe MacLaren Johannes KollmannSammendrag
No abstract has been registered
Sammendrag
Forest inventories provide predictions of stand means on a routine basis from models with auxiliary variables from remote sensing as predictors and response variables from field data. Many forest inventory sampling designs do not afford a direct estimation of the among-stand variance. As consequence, the confidence interval for a model-based prediction of a stand mean is typically too narrow. We propose a new method to compute (from empirical regression residuals) an among-stand variance under sample designs that stratify sample selections by an auxiliary variable, but otherwise do not allow a direct estimation of this variance. We test the method in simulated sampling from a complex artificial population with an age class structure. Two sampling designs are used (one-per-stratum, and quasi systematic), neither recognize stands. Among-stand estimates of variance obtained with the proposed method underestimated the actual variance by 30-50%, yet 95% confidence intervals for a stand mean achieved a coverage that was either slightly better or at par with the coverage achieved with empirical linear best unbiased estimates obtained under less efficient two-stage designs.